ReviewNature food2023
Large language models and agricultural extension services.
Review in Nature food, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 87 citations in OpenAlex.
- Applications, Challenges, and Prospects of Artificial Intelligence in Crop Production.Plants (Basel, Switzerland) · 2026Review
- Sharing big data for sustainable agri-food innovation.Nature biotechnology · 2026Article
- Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support.Plant phenomics (Washington, D.C.) · 2026Review
- The development and evaluation of agricultural question-answering systems based on large language models.Scientific reports · 2026Article
- Generative AI use and advisory performance among agricultural extension agents in Benin.Frontiers in artificial intelligence · 2026Article
- A condition-aware retrieval-augmented decision support framework for tomato cultivation management.Frontiers in plant science · 2026Article
- Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India.Frontiers in artificial intelligence · 2026Review
- Retrieval-grounded multilingual LLM assistance for island smallholder farmers.Open research Europe · 2026Article
- Visual-language transformer-based tomato leaf disease detection for portable greenhouse monitoring device.Plant methods · 2025Article
- ChatLeafDisease: a chain-of-thought prompting approach for crop disease classification using large language models.Plant phenomics (Washington, D.C.) · 2025Article
- PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention.Plant methods · 2025Article
- Factors influencing the agricultural extension model sites in Iran.Scientific reports · 2025Article
- Advancements in Agricultural Ground Robots for Specialty Crops: An Overview of Innovations, Challenges, and Prospects.Plants (Basel, Switzerland) · 2024Review
- Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Evaluating responses by ChatGPT to farmers' questions on irrigated lowland rice cultivation in Nigeria.Scientific reports · 2024Article
- Large language models can help boost food production, but be mindful of their risks.Frontiers in artificial intelligence · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 7 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several factors have traditionally hampered the effectiveness of agricultural extension services, including limited institutional capacity and reach. Here we assess the potential of large language models (LLMs), specifically Generative Pre-trained Transformer (GPT), to transform agricultural extension. We focus on the ability of LLMs to simplify scientific knowledge and provide personalized, location-specific and data-driven agricultural recommendations. We emphasize shortcomings of this technology, informed by real-life testing of GPT to generate technical advice for Nigerian cassava farmers. To ensure a safe and responsible dissemination of LLM functionality across farming worldwide, we propose an idealized LLM design process with human experts in the loop.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.